The purpose of this exercise is to check the maximum sea surface temparature near

  • Key West (24.545°N, 81.775°W)
  • Barbados (13.185° N, -59.440° W)
  • Porto (41.130° N, -8.882° W)?


1.1. Data: sdmpredictors

Where can we look for the data? http://www.bio-oracle.org/

1.1.2 prepare R environment

First, we load some R packages that we will use in this exercise ```

library(sf) # spatial package
library(ggplot2) # plotting
library(sdmpredictors) # species distribution modeling layers => data layers

Now, we want to use the data from the sdmpredictors package

1.1.2 Look for correct data layer

sdmpredictors contains several datasets, each containing several layers:

# list datasets of sdmpredictors package
list_datasets()
# list layers of Bio-ORACLE dataset
list_layers(datasets="Bio-ORACLE")
1.1.3 Download layer

Now we store the data from the layer to R:

sstmax <- load_layers("BO_sstmax")


1.2 Plotting data

1.2.1 Base R Plot

Let’s try R’s base plot:

# base plot
plot(sstmax)

This is not so user friendly, let’s try to make an interactive map.

1.2.2 Mapview interactive map

With the ‘mapview’ package, you can easily create interactive maps. For more info, see https://r-spatial.github.io/mapview/

# Make an interactive plot with the 'mapview' package:
# more info: https://r-spatial.github.io/mapview/
library('mapview')

if we just try mapview(sstmax), there is an error. What does it say?

mapview(sstmax, layer.name = "max sst") # warning: what does it say?
mapview(sstmax, layer.name = "max sst", maxpixels =  9331200) # takes a while to load!


1.3 Points of interest

1.3.1 create dataframe

Let’s create a dataframe with our points of interest:

my.sites <- data.frame(
  Name = c("Key West", "Barbados", "Porto"),
  Lon = c(-81.775,-59.440,-8.882),
  Lat = c(24.545,13.185,41.130))
1.3.2 make spatial ‘simple features’
# make spatial (simple features) dataset
my.sites.sf <- st_as_sf(my.sites,
                        coords=c("Lon","Lat"),
                        crs = 4326) # WGS84
1.3.3 base plot locations

Again, R’s base plotting capabilities are not ideal…

# base plot
plot(my.sites.sf) # not ideal

##### 1.3.3 interactive mapview map An interactive map is a lot better, right?

# interactive mapview map
mapview(my.sites.sf) # much better
1.3.3 visualizing both layers

How do we visualize both of the layers? Just combine them with a plus sign: ‘+’:

# how to visualize both? combine them with '+'
mapview(sstmax, layer.name = "max sst") +
  mapview(my.sites.sf)


1.4 extracting data

Now, we want to get the sea surface temperature data at the exact location of our points. This is easily done with the raster package function extract:

# extract data from sstmax raster at the locations of my.sites.sf
my.sites$BO_sstmax <- extract(sstmax,my.sites.sf)


1.5 Plotting data in ggplot

We will plot the data that we have with the ggplot2 package: https://ggplot2.tidyverse.org/

The grammar of graphics say that can build any graph with the same components:

  • the data(set)
  • aesthetics: how you position your data
  • a geometry: how to visualize the data points
  • (some additional styling)
# plotting data with ggplot2
# https://ggplot2.tidyverse.org/
library(ggplot2)

Let’s create an easy example with

  • data: my.sites
  • aesthetics: along x-axis the Name column, y-axis BO_sstmax (the temperature)
  • geometry: points
# points
ggplot(data = my.sites) +
  geom_point(aes(x = Name, y = BO_sstmax))

If we improve the graph by:

  • color filling the points by the sea surface temperature value
  • use the same color scale as the sst map
  • make the pionts a bit bigger
  # with different colors, a bit larger size
  ggplot(data = my.sites) +
    geom_point(aes(x = Name,
                   y = BO_sstmax,
                   color = BO_sstmax,
                   size = 2)) +
    scale_color_viridis_c(option = 'inferno') # same colorscale as map

We can change the geometry to a barplot:

# bar plot
ggplot(data = my.sites) +
  geom_col(aes(x = Name, y = BO_sstmax))

different colors, larger size (outline width):

  # again different colors, larger size:
  ggplot(data = my.sites) +
    geom_col(aes(x = Name,        # everything inside aes(), will be 'interpreted'
                 y = BO_sstmax,
                 fill = Name),
                 size = 2,
                 color = "black") # color : outline with graphs

Some more adjustments

  # color by sst max
  ggplot(data = my.sites) +
    geom_col(aes(x = Name,        # everything inside aes(), will be 'interpreted'
                 y = BO_sstmax,
                 fill = BO_sstmax),
             color = "black") +
             scale_fill_viridis_c(option = 'inferno') # color : outline with graphs


Exercise 1

Adjust the code above to include 3 areas of your interest

e.g. the location closest you your lab?

Advanced exercise 1

What is the temperature at different latitudes in the North Atlantic?

e.g. at longitude -30, latitude from 0 to 60?

Advanced exercise 2

What is the minimum temperature? Can you plot:

  • the min temperature vs max temperature
  • min AND max temp in a plot?